Publication Details

Category Text Publication
Reference Category Journals
DOI 10.1021/acs.est.2c02232
Title (Primary) Predicting dynamic riverine nitrogen export in unmonitored watersheds: Leveraging insights of AI from data-rich regions
Author Xiong, R.; Zheng, Y.; Chen, N.; Tian, Q.; Liu, W.; Han, F.; Jiang, S.; Lu, M.; Zheng, Y.
Source Titel Environmental Science & Technology
Year 2022
Department CHS
Volume 56
Issue 14
Page From 10530
Page To 10542
Language englisch
Topic T5 Future Landscapes
Keywords nitrogen; riverine export; nonpoint sources; deep learning; LSTM; transfer learning; artificial intelligence
Abstract Terrestrial export of nitrogen is a critical Earth system process, but its global dynamics remain difficult to predict at a high spatiotemporal resolution. Here, we use deep learning (DL) to model daily riverine nitrogen export in response to hydrometeorological and anthropogenic drivers. Long short-term memory (LSTM) models for the daily concentration and flux of dissolved inorganic nitrogen (DIN) were built in a coastal watershed in southeastern China with a typical subtropical monsoon climate. The DL models exhibited excellent accuracy for both DIN concentration and flux, with Nash-Sutcliffe efficiency coefficients (NSEs) up to 0.67 and 0.92, respectively, a performance unlikely to be achieved by generic process-based models with comparable data quality. The flux model ensemble, without retraining, performed well (mean NSE = 0.32–0.84) in seven distinct watersheds in Asia, Europe, and North America, and retraining with multi-watershed data further improved the lowest NSE from 0.32 to 0.68. DL interpretation confirmed that interbasin consistency of riverine nitrogen export exists across different continents, which stems from the similarities in rainfall–runoff relationships. The multi-watershed flux model projects 0.60–12.4% increases in the nitrogen export to oceans from the studied watersheds under a 20% increase in fertilizer consumption, which rises to 6.7–20.1% with a 10% increase in runoff, indicating the synergistic effect of human activities and climate change. The DL-based method represents a successful case of explainable artificial intelligence in environmental science, providing a potential shortcut to a consistent understanding of the global daily-resolution dynamics of riverine nitrogen export under the currently limited data conditions.
Persistent UFZ Identifier
Xiong, R., Zheng, Y., Chen, N., Tian, Q., Liu, W., Han, F., Jiang, S., Lu, M., Zheng, Y. (2022):
Predicting dynamic riverine nitrogen export in unmonitored watersheds: Leveraging insights of AI from data-rich regions
Environ. Sci. Technol. 56 (14), 10530 - 10542 10.1021/acs.est.2c02232